This paper studies small PV-rich communities that wish to purchase their electricity directly from generators through long-term power purchase agreements (PPA) and the spot market if necessary. Net demand, defined as the difference between total demand and solar generation, is satisfied through PPA and the spot market. Due to the random nature of solar generation, net demand is random and hard to estimate. This randomness may lead to a mismatch between the actual net demand and PPA. When net demand exceeds PPA, electricity is purchased at the spot market prices causing increased operational costs. To avoid this mismatch and utilise solar potential efficiently, we propose a probabilistic optimisation approach using the statistical properties of solar generation. We model solar energy generation as a random variable for each daylight hour at a chosen location using historical solar irradiance data. By transforming random variables, we find the probability distribution of the net demand which is used to propose a market exposure probability limiting optimisation problem. The overall objective of the optimisation problem is to minimise operational costs of communities. Unlike forecasting and Monte Carlo Simulation, our methodology enables market exposure risk analysis, fine-tuning PPA and a good understanding of statistical properties of solar generation.